In this paper, we consider real-world applications of IM in which the underlying network \(G\)  is initially unknown. Motivated by the fact that collecting metadata of nodes such as node features is cost-effective, we present IM-META, an end-to-end framework for effectively solving the IM problem in \(G\)  using node queries aided by node metadata. To this end, we aim at retrieving information from jointly discovering a sequence of node queries and a set of nodes maximizing the influence spread. However, unlike the conventional setting of IM [1]}, [2]}, [3]}, [4]}, our problem is ill-defined due to the uncertainty of the unexplored part of \(G\)  that is not obtained from node queries. In our study, we resolve this issue by making full use of the available node metadata in order to infer the connections between two nodes in the unexplored part. From this inferred network structure, it is possible to formulate a new optimization problem that jointly finds the optimal discovered subgraph and seed set in the sense of maximizing an estimate of the expected influence spread.
